Applying Fuzzy Ontologies to Implement the Social Semantic Web

Because the knowledge in the World Wide Web is continuously expanding, Web Knowledge Aggregation, Representation and Reasoning (abbreviated as KR) is becoming increasingly important. This article demonstrates how fuzzy ontologies can be used in KR to improve the interactions between humans and computers. The gap between the Social and Semantic Web can be reduced, and a Social Semantic Web may become possible. As an illustrative example, we demonstrate how fuzzy logic and KR can enhance technologies for cognitive cities. The underlying notion of these technologies is based on connectivism, which can be improved by incorporating the results of digital humanities research.

Dynamics of governance and control in inter-organizational software development

In recent years, development of information systems (IS) has rapidly changed towards increasing division of labor between firms. Two trends are emerging. First, client companies increasingly outsource software development to external service providers. Second, the formerly oligopolistic enterprise application software industry has started to disintegrate into focal partnership networks – so called platform ecosystems. Despite the increasing prominence of IS outsourcing and platform ecosystems, many of these inter-organizational partnerships fail to achieve expected benefits. Ineffective governance and control frequently plays a pivotal role in producing these failures. While designing effective governance and control mechanisms is always challenging, inter-organizational software development projects are often business-critical and exhibit additional dynamics and uncertainty. As a consequence governance and control have to be adapted over time. The three research projects included in this book provide a better understanding of how and why governance and control can be effectively adapted over time. The implications for successful management of inter-organizational software development projects are highly relevant for theory and practice.

Explaining Emergence and Consequences of Specific Formal Controls in IS Outsourcing – A Process-View

IS outsourcing projects often fail to achieve project goals. To inhibit this failure, managers need to design formal controls that are tailored to the specific contextual demands. However, the dynamic and uncertain nature of IS outsourcing projects makes the design of such specific formal controls at the outset of a project challenging. Hence, the process of translating high-level project goals into specific formal controls becomes crucial for success or failure of IS outsourcing projects. Based on a comparative case study of four IS outsourcing projects, our study enhances current understanding of such translation processes and their consequences by developing a process model that explains the success or failure to achieve high-level project goals as an outcome of two unique translation patterns. This novel process-based explanation for how and why IS outsourcing projects succeed or fail has important implications for control theory and IS project escalation literature.

The Software Prototype as Digital Boundary Object – A Revelatory Longitudinal Innovation Case

With the availability of lower cost but highly skilled software development labor from offshore regions, entrepreneurs from developed countries who do not have software development experience can utilize this workforce to develop innovative software products. In order to succeed in offshored innovation projects, the often extreme knowledge boundaries between the onsite entrepreneur and the offshore software development team have to be overcome. Prior research has proposed that boundary objects are critical for bridging such boundaries – if they are appropriately used. Our longitudinal, revelatory case study of a software innovation project is one of the first to explore the role of the software prototype as a digital boundary object. Our study empirically unpacks five use practices that transform the software prototype into a boundary object such that knowledge boundaries are bridged. Our findings provide new theoretical insights for literature on software innovation and boundary objects, and have implications for practice.

Mechanisms of Ligand–Protein Interaction in Sec-14-like Transporters Investigated by Computer Simulations

We review our recent work on protein-ligand interactions in vitamin transporters of the Sec-14-like protein. Our studies focused on the cellular-retinaldehyde binding protein (CRALBP) and the alpha-tocopherol transfer protein (alpha-TTP). CRALBP is responsible for mobilisation and photo-protection of short-chain cis-retinoids in the dim-light visual cycle or rod photoreceptors. alpha-TTP is a key protein responsible for selection and retention of RRR-alpha-tocopherol, the most active isoform of vitamin E in superior animals. Our simulation studies evidence how subtle chemical variations in the substrate can lead to significant distortion in the structure of the complex, and how these changes can either lead to new protein function, or be used to model engineered protein variants with tailored properties. Finally, we show how integration of computational and experimental results can contribute in synergy to the understanding of fundamental processes at the biomolecular scale.

A Hennessy-Milner Property for Many-Valued Modal Logics

A Hennessy-Milner property, relating modal equivalence and bisimulations, is defined for many-valued modal logics that combine a local semantics based on a complete MTL-chain (a linearly ordered commutative integral residuated lattice) with crisp Kripke frames. A necessary and sufficient algebraic condition is then provided for the class of image-finite models of these logics to admit the Hennessy-Milner property. Complete characterizations are obtained in the case of many-valued modal logics based on BL-chains (divisible MTL-chains) that are finite or have universe [0,1], including crisp Lukasiewicz, Gödel, and product modal logics.

Image registration for triggered and non-triggered DTI of the human kidney: Reduced variability of diffusion parameter estimation

BACKGROUND: To investigate if non-rigid image-registration reduces motion artifacts in triggered and non-triggered diffusion tensor imaging (DTI) of native kidneys. A secondary aim was to determine, if improvements through registration allow for omitting respiratory-triggering. METHODS: Twenty volunteers underwent coronal DTI of the kidneys with nine b-values (10-700 s/mm2 ) at 3 Tesla. Image-registration was performed using a multimodal nonrigid registration algorithm. Data processing yielded the apparent diffusion coefficient (ADC), the contribution of perfusion (FP ), and the fractional anisotropy (FA). For comparison of the data stability, the root mean square error (RMSE) of the fitting and the standard deviations within the regions of interest (SDROI ) were evaluated. RESULTS: RMSEs decreased significantly after registration for triggered and also for non-triggered scans (P < 0.05). SDROI for ADC, FA, and FP were significantly lower after registration in both medulla and cortex of triggered scans (P < 0.01). Similarly the SDROI of FA and FP decreased significantly in non-triggered scans after registration (P < 0.05). RMSEs were significantly lower in triggered than in non-triggered scans, both with and without registration (P < 0.05). CONCLUSION: Respiratory motion correction by registration of individual echo-planar images leads to clearly reduced signal variations in renal DTI for both triggered and particularly non-triggered scans. Secondarily, the results suggest that respiratory-triggering still seems advantageous.J. Magn. Reson. Imaging 2014. (c) 2014 Wiley Periodicals, Inc.

Patient-Specific Semi-Supervised Learning for Postoperative Brain Tumor Segmentation

In contrast to preoperative brain tumor segmentation, the problem of postoperative brain tumor segmentation has been rarely approached so far. We present a fully-automatic segmentation method using multimodal magnetic resonance image data and patient-specific semi-supervised learning. The idea behind our semi-supervised approach is to effectively fuse information from both pre- and postoperative image data of the same patient to improve segmentation of the postoperative image. We pose image segmentation as a classification problem and solve it by adopting a semi-supervised decision forest. The method is evaluated on a cohort of 10 high-grade glioma patients, with segmentation performance and computation time comparable or superior to a state-of-the-art brain tumor segmentation method. Moreover, our results confirm that the inclusion of preoperative MR images lead to a better performance regarding postoperative brain tumor segmentation.

Interactive Segmentation of MR Images from Brain Tumor Patients

Medical doctors often do not trust the result of fully automatic segmentations because they have no possibility to make corrections if necessary. On the other hand, manual corrections can introduce a user bias. In this work, we propose to integrate the possibility for quick manual corrections into a fully automatic segmentation method for brain tumor images. This allows for necessary corrections while maintaining a high objectiveness. The underlying idea is similar to the well-known Grab-Cut algorithm, but here we combine decision forest classification with conditional random field regularization for interactive segmentation of 3D medical images. The approach has been evaluated by two different users on the BraTS2012 dataset. Accuracy and robustness improved compared to a fully automatic method and our interactive approach was ranked among the top performing methods. Time for computation including manual interaction was less than 10 minutes per patient, which makes it attractive for clinical use.